Results 211 to 220 of about 27,240 (263)

Parametric Analysis of Spiking Neurons in 16 nm Fin Field‐Effect Transistor Technology

Advanced Intelligent Discovery, EarlyView.
Energy efficient computing has driven a shift toward brain‐inspired neuromorphic hardware. This study explores the design of three distinct silicon neuron topologies implemented in 16 nm fin field‐Effect transistor technology. While the Axon‐Hillock design achieves gigahertz throughput, its functional fragility persists. The Morris–Lecar model captures
Logan Larsh, Raiyan Siddique, Sarah S. Sharif, Yaser Mike Banad   +3 more
wiley   +1 more source

A robust Parkinson's disease detection model based on time-varying synaptic efficacy function in spiking neural network. [PDF]

BMC Neurol
Das P, Nanda S, Panda G, Dash S, Ksibi A, Alsenan S, Bouchelligua W, Mallik S.   +7 more
europepmc   +1 more source

Training spiking neural networks

, 2023
openaire   +1 more source

Dual‐Mode Oscillations in SiOx‐Based Threshold Switching Devices for Unconventional Probabilistic Computing

Advanced Intelligent Discovery, EarlyView.
To enable versatile unconventional computing, a single SiOx threshold switching device is engineered to exhibit controllable dual‐mode oscillation. By modulating the input voltage, the device selectively provides stable full oscillation for oscillatory neural networks and stochastic probabilistic oscillation for probabilistic bits and true random ...
Hyeonsik Choi, Jihyun Kim, Yonghun Kim, Jiyong Woo   +3 more
wiley   +1 more source

Clinically Informed Intelligent Classification of Ovarian Cancer Cells by Label‐Free Holographic Imaging Flow Cytometry

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
Quantitative phase maps of single cells recorded in flow cytometry modality feed a hierarchical architecture of machine learning models for the label‐free identification of subtypes of ovarian cancer. The employment of a priori clinical information improves the classification performance, thus emulating the clinical application of liquid biopsy during ...
Daniele Pirone, Beatrice Cavina, Daniele Gaetano Sirico, Martina Mugnano, Vittorio Bianco, Lisa Miccio, Anna Myriam Perrone, Anna Maria Porcelli, Giuseppe Gasparre, Ivana Kurelac, Pasquale Memmolo, Pietro Ferraro   +11 more
wiley   +1 more source

Low-power Spiking Neural Network audio source localisation using a Hilbert Transform audio event encoding scheme. [PDF]

Commun Eng
Haghighatshoar S, Muir DR.
europepmc   +1 more source

Haptic In‐Sensor Computing Device Based on CNT/PDMS Nanocomposite Physical Reservoir

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
Using a porous carbon nanotube‐polydimethylsiloxane nanocomposite, a sensor array integrated with a physical reservoir computing paradigm capable of in‐sensor computing is demonstrated. The device is able to classify between nine objects with an accuracy above 80%, opening the possibility for low‐power sensing/computing for future robotics.
Kouki Kimizuka, Saman Azhari, Shoshi Tokuno, Ahmet Karacali, Yuki Usami, Shuhei Ikemoto, Hakaru Tamukoh, Hirofumi Tanaka   +7 more
wiley   +1 more source

[A head direction cell model based on a spiking neural network with landmark-free calibration]. [PDF]

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi
Yu N, Huang J, Lin K, Zhang Z.
europepmc   +1 more source

BayesianSpikeFusion: accelerating spiking neural network inference via Bayesian fusion of early prediction. [PDF]

Front Neurosci
Habara T, Sato T, Awano H.
europepmc   +1 more source

Estimating orientation in natural scenes: A spiking neural network model of the insect central complex. [PDF]

PLoS Comput Biol
Stentiford R, Knight JC, Nowotny T, Philippides A, Graham P.   +4 more
europepmc   +1 more source